Inference for a Variance: How Robust are These Procedures? @jbstatistics

Uploaded June 2014 | Updated September 2026, 3 hours ago

A discussion of the effect of violations of the normality assumption on confidence intervals for a variance. The effects of different violations of the normality assumption are investigated through simulation. The quick summary: These procedures are very sensitive to violations of the normality assumption, and often perform very poorly when the normality assumption is violated.

Inference for a Variance: How Robust are These Procedures?

Hypothesis Testing: Introduction | Full Lecture (Intro Stats)

Simple Linear Regression: Checking Assumptions with Residual Plots (Old, fast version)

Statistical Significance versus Practical Significance

The Bernoulli Distribution: Deriving the Mean and Variance

Confidence Intervals for One Mean: Sigma Not Known (t Method)

Introduction to the Bernoulli Distribution

An Introduction to Conditional Probability

Proof that if two events are independent, so are their complements.

Proof that the Binomial Distribution tends to the Poisson Distribution

Simple Linear Regression: The Least Squares Regression Line

Hypothesis testing: Rejection regions and p-values |Full lecture (Intro Stats)